Incorporating g-gap dipeptide composition and position specific scoring matrix for identifying antioxidant proteins

Oxidative stress can damage major cell components, including protein, DNA, lipid and cell membranes, which may make cells lose function and induce a wide variety of diseases. As an extensive kind of antioxidants in human and animals, antioxidant proteins are essential to eliminate cell damage and aging problems caused by oxidative stress. Accurate identification of antioxidant proteins is a significant step to reveal the inducement and physiological process of certain types of diseases and aging. Furthermore, newly identified antioxidant proteins may provide candidate targets for curing or alleviating diseases and slowing down the aging process. In this study, a random forest-based approach incorporating PSSM (Position Specific Scoring Matrix) and g-gap dipeptide composition is put forward to distinguish antioxidant proteins from non-antioxidant proteins. To further improve the prediction performance, the information gain combined with incremental feature selection is adopted to obtain optimal features. Compared with prior studies in testing dataset, the proposed method shows excellent predictive performance with accuracy of 0.807, MCC of 0.543, AUC of 0.939, respectively. It is indicated that this method may be an alternative perspective predictor for annotating antioxidant proteins.